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Article: Iterative statistical approach to blind image deconvolution

TitleIterative statistical approach to blind image deconvolution
Authors
Issue Date2000
Citation
Journal Of The Optical Society Of America A: Optics And Image Science, And Vision, 2000, v. 17 n. 7, p. 1177-1184 How to Cite?
AbstractImage deblurring has long been modeled as a deconvolution problem. In the literature, the point-spread function (PSF) is often assumed to be known exactly. However, in practical situations such as image acquisition in cameras, we may have incomplete knowledge of the PSF. This deblurring problem is referred to as blind deconvolution. We employ a statistical point of view of the data and use a modified maximum a posteriori approach to identify the most probable object and blur given the observed image. To facilitate computation we use an iterative method, which is an extension of the traditional expectation-maximization method, instead of direct optimization. We derive separate formulas for the updates of the estimates in each iteration to enhance the deconvolution results, which are based on the specific nature of our a priori knowledge available about the object and the blur. © 2000 Optical Society of America.
Persistent Identifierhttp://hdl.handle.net/10722/154779
ISSN
2002 Impact Factor: 1.688
References

 

DC FieldValueLanguage
dc.contributor.authorLam, EYen_US
dc.contributor.authorGoodman, JWen_US
dc.date.accessioned2012-08-08T08:30:37Z-
dc.date.available2012-08-08T08:30:37Z-
dc.date.issued2000en_US
dc.identifier.citationJournal Of The Optical Society Of America A: Optics And Image Science, And Vision, 2000, v. 17 n. 7, p. 1177-1184en_US
dc.identifier.issn0740-3232en_US
dc.identifier.urihttp://hdl.handle.net/10722/154779-
dc.description.abstractImage deblurring has long been modeled as a deconvolution problem. In the literature, the point-spread function (PSF) is often assumed to be known exactly. However, in practical situations such as image acquisition in cameras, we may have incomplete knowledge of the PSF. This deblurring problem is referred to as blind deconvolution. We employ a statistical point of view of the data and use a modified maximum a posteriori approach to identify the most probable object and blur given the observed image. To facilitate computation we use an iterative method, which is an extension of the traditional expectation-maximization method, instead of direct optimization. We derive separate formulas for the updates of the estimates in each iteration to enhance the deconvolution results, which are based on the specific nature of our a priori knowledge available about the object and the blur. © 2000 Optical Society of America.en_US
dc.languageengen_US
dc.relation.ispartofJournal of the Optical Society of America A: Optics and Image Science, and Visionen_US
dc.titleIterative statistical approach to blind image deconvolutionen_US
dc.typeArticleen_US
dc.identifier.emailLam, EY:elam@eee.hku.hken_US
dc.identifier.authorityLam, EY=rp00131en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0013464047en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0013464047&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume17en_US
dc.identifier.issue7en_US
dc.identifier.spage1177en_US
dc.identifier.epage1184en_US
dc.identifier.scopusauthoridLam, EY=7102890004en_US
dc.identifier.scopusauthoridGoodman, JW=7402288924en_US

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